Overview

Dataset statistics

Number of variables13
Number of observations343297
Missing cells12
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 MiB
Average record size in memory104.0 B

Variable types

Numeric9
Categorical4

Alerts

DATA_AQUISICAO has a high cardinality: 1956 distinct valuesHigh cardinality
DIAS_ATIVO is highly overall correlated with MESES_ATIVO and 3 other fieldsHigh correlation
MESES_ATIVO is highly overall correlated with DIAS_ATIVO and 3 other fieldsHigh correlation
QT_PC_PAGAS is highly overall correlated with DIAS_ATIVO and 4 other fieldsHigh correlation
QT_PC_VENCIDAS is highly overall correlated with DIAS_ATIVO and 4 other fieldsHigh correlation
QT_PC_PAGA_EM_DIA is highly overall correlated with QT_PC_PAGAS and 2 other fieldsHigh correlation
VALOR_PARCELAS_ATRASO is highly overall correlated with DIAS_ATIVO and 4 other fieldsHigh correlation
PRAZO_FINANCIAMENTO is highly overall correlated with VALOR_TABELA and 1 other fieldsHigh correlation
VALOR_TABELA is highly overall correlated with PRAZO_FINANCIAMENTO and 1 other fieldsHigh correlation
PRODUTO is highly overall correlated with PRAZO_FINANCIAMENTO and 1 other fieldsHigh correlation
QT_PC_VENCIDAS has 241626 (70.4%) zerosZeros
QT_PC_PAGA_ATRASO has 218566 (63.7%) zerosZeros
QT_PC_PAGA_EM_DIA has 3716 (1.1%) zerosZeros
VALOR_PARCELAS_ATRASO has 241626 (70.4%) zerosZeros

Reproduction

Analysis started2023-05-10 18:33:43.203677
Analysis finished2023-05-10 18:35:34.978885
Duration1 minute and 51.78 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ID_CLIENTE
Real number (ℝ)

Distinct342336
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1091769 × 108
Minimum2906
Maximum5.7111888 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:35.586323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2906
5-th percentile418399.6
Q1599957
median1935123
Q329910640
95-th percentile5.7109624 × 108
Maximum5.7111888 × 108
Range5.7111597 × 108
Interquartile range (IQR)29310683

Descriptive statistics

Standard deviation2.17307 × 108
Coefficient of variation (CV)1.9591735
Kurtosis0.7015929
Mean1.1091769 × 108
Median Absolute Deviation (MAD)1466538
Skewness1.6384652
Sum3.807771 × 1013
Variance4.7222333 × 1016
MonotonicityNot monotonic
2023-05-10T15:35:36.419428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
495783 2
 
< 0.1%
515146 2
 
< 0.1%
513827 2
 
< 0.1%
516007 2
 
< 0.1%
493418 2
 
< 0.1%
495820 2
 
< 0.1%
504634 2
 
< 0.1%
504368 2
 
< 0.1%
456650 2
 
< 0.1%
513596 2
 
< 0.1%
Other values (342326) 343277
> 99.9%
ValueCountFrequency (%)
2906 1
< 0.1%
3358 1
< 0.1%
3392 1
< 0.1%
3411 1
< 0.1%
3453 1
< 0.1%
5730 1
< 0.1%
11921 1
< 0.1%
12539 1
< 0.1%
13124 1
< 0.1%
14352 1
< 0.1%
ValueCountFrequency (%)
571118875 1
< 0.1%
571118873 1
< 0.1%
571118855 1
< 0.1%
571118854 1
< 0.1%
571118853 1
< 0.1%
571118852 1
< 0.1%
571118850 1
< 0.1%
571118842 1
< 0.1%
571118841 1
< 0.1%
571118839 1
< 0.1%

DATA_AQUISICAO
Categorical

Distinct1956
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
30/11/2020
 
1274
31/07/2020
 
1208
30/09/2019
 
1203
31/10/2019
 
1201
02/09/2019
 
1188
Other values (1951)
337223 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3432970
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)< 0.1%

Sample

1st row18/06/2021
2nd row10/04/2018
3rd row09/10/2020
4th row25/06/2019
5th row19/09/2019

Common Values

ValueCountFrequency (%)
30/11/2020 1274
 
0.4%
31/07/2020 1208
 
0.4%
30/09/2019 1203
 
0.4%
31/10/2019 1201
 
0.3%
02/09/2019 1188
 
0.3%
01/03/2021 1113
 
0.3%
14/03/2018 1091
 
0.3%
29/10/2019 1013
 
0.3%
31/03/2021 942
 
0.3%
03/10/2018 930
 
0.3%
Other values (1946) 332134
96.7%

Length

2023-05-10T15:35:37.205825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30/11/2020 1274
 
0.4%
31/07/2020 1208
 
0.4%
30/09/2019 1203
 
0.4%
31/10/2019 1201
 
0.3%
02/09/2019 1188
 
0.3%
01/03/2021 1113
 
0.3%
14/03/2018 1091
 
0.3%
29/10/2019 1013
 
0.3%
31/03/2021 942
 
0.3%
03/10/2018 930
 
0.3%
Other values (1946) 332134
96.7%

Most occurring characters

ValueCountFrequency (%)
0 868538
25.3%
/ 686594
20.0%
2 680312
19.8%
1 544970
15.9%
9 150507
 
4.4%
8 135974
 
4.0%
3 91015
 
2.7%
5 76495
 
2.2%
6 74063
 
2.2%
7 65621
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2746376
80.0%
Other Punctuation 686594
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 868538
31.6%
2 680312
24.8%
1 544970
19.8%
9 150507
 
5.5%
8 135974
 
5.0%
3 91015
 
3.3%
5 76495
 
2.8%
6 74063
 
2.7%
7 65621
 
2.4%
4 58881
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 686594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3432970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 868538
25.3%
/ 686594
20.0%
2 680312
19.8%
1 544970
15.9%
9 150507
 
4.4%
8 135974
 
4.0%
3 91015
 
2.7%
5 76495
 
2.2%
6 74063
 
2.2%
7 65621
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3432970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 868538
25.3%
/ 686594
20.0%
2 680312
19.8%
1 544970
15.9%
9 150507
 
4.4%
8 135974
 
4.0%
3 91015
 
2.7%
5 76495
 
2.2%
6 74063
 
2.2%
7 65621
 
1.9%

DIAS_ATIVO
Real number (ℝ)

Distinct1050
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean520.94136
Minimum22
Maximum1296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:38.007197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile70
Q1170
median405
Q3841
95-th percentile1204
Maximum1296
Range1274
Interquartile range (IQR)671

Descriptive statistics

Standard deviation379.75291
Coefficient of variation (CV)0.72897439
Kurtosis-1.1289618
Mean520.94136
Median Absolute Deviation (MAD)254
Skewness0.49550853
Sum1.788376 × 108
Variance144212.27
MonotonicityNot monotonic
2023-05-10T15:35:38.956570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161 3346
 
1.0%
168 3270
 
1.0%
175 3240
 
0.9%
169 2858
 
0.8%
174 2729
 
0.8%
167 2678
 
0.8%
162 2605
 
0.8%
176 2547
 
0.7%
173 2370
 
0.7%
153 2347
 
0.7%
Other values (1040) 315307
91.8%
ValueCountFrequency (%)
22 673
0.2%
23 536
0.2%
24 1
 
< 0.1%
25 5
 
< 0.1%
26 577
0.2%
27 311
0.1%
28 622
0.2%
29 522
0.2%
30 508
0.1%
33 442
0.1%
ValueCountFrequency (%)
1296 642
0.2%
1295 205
 
0.1%
1294 363
0.1%
1293 165
 
< 0.1%
1292 6
 
< 0.1%
1290 318
0.1%
1289 210
 
0.1%
1288 243
 
0.1%
1287 217
 
0.1%
1286 257
0.1%

MESES_ATIVO
Real number (ℝ)

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.968756
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:39.867199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median13
Q327
95-th percentile39
Maximum42
Range41
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.449125
Coefficient of variation (CV)0.73364982
Kurtosis-1.1287235
Mean16.968756
Median Absolute Deviation (MAD)8
Skewness0.49296737
Sum5825323
Variance154.98071
MonotonicityNot monotonic
2023-05-10T15:35:40.659999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5 44561
 
13.0%
6 27025
 
7.9%
4 12094
 
3.5%
2 10753
 
3.1%
1 10155
 
3.0%
8 9924
 
2.9%
12 9707
 
2.8%
3 9217
 
2.7%
10 8632
 
2.5%
7 8473
 
2.5%
Other values (32) 192756
56.1%
ValueCountFrequency (%)
1 10155
 
3.0%
2 10753
 
3.1%
3 9217
 
2.7%
4 12094
 
3.5%
5 44561
13.0%
6 27025
7.9%
7 8473
 
2.5%
8 9924
 
2.9%
9 8298
 
2.4%
10 8632
 
2.5%
ValueCountFrequency (%)
42 5669
1.7%
41 4271
1.2%
40 6478
1.9%
39 5386
1.6%
38 5524
1.6%
37 5034
1.5%
36 5557
1.6%
35 6452
1.9%
34 6249
1.8%
33 6275
1.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
120
184412 
60
110786 
240
37460 
36
 
10639

Length

Max length3
Median length3
Mean length2.6462975
Min length2

Characters and Unicode

Total characters908466
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row120
3rd row120
4th row120
5th row120

Common Values

ValueCountFrequency (%)
120 184412
53.7%
60 110786
32.3%
240 37460
 
10.9%
36 10639
 
3.1%

Length

2023-05-10T15:35:41.377132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T15:35:42.432377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
120 184412
53.7%
60 110786
32.3%
240 37460
 
10.9%
36 10639
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 332658
36.6%
2 221872
24.4%
1 184412
20.3%
6 121425
 
13.4%
4 37460
 
4.1%
3 10639
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 908466
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 332658
36.6%
2 221872
24.4%
1 184412
20.3%
6 121425
 
13.4%
4 37460
 
4.1%
3 10639
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 908466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 332658
36.6%
2 221872
24.4%
1 184412
20.3%
6 121425
 
13.4%
4 37460
 
4.1%
3 10639
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 908466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 332658
36.6%
2 221872
24.4%
1 184412
20.3%
6 121425
 
13.4%
4 37460
 
4.1%
3 10639
 
1.2%

VALOR_TABELA
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
80000
245218 
300000
48476 
20000
42447 
230000
 
7156

Length

Max length6
Median length5
Mean length5.1620521
Min length5

Characters and Unicode

Total characters1772117
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20000
2nd row80000
3rd row230000
4th row80000
5th row80000

Common Values

ValueCountFrequency (%)
80000 245218
71.4%
300000 48476
 
14.1%
20000 42447
 
12.4%
230000 7156
 
2.1%

Length

2023-05-10T15:35:43.132525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T15:35:43.921114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
80000 245218
71.4%
300000 48476
 
14.1%
20000 42447
 
12.4%
230000 7156
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1421664
80.2%
8 245218
 
13.8%
3 55632
 
3.1%
2 49603
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1772117
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1421664
80.2%
8 245218
 
13.8%
3 55632
 
3.1%
2 49603
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1772117
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1421664
80.2%
8 245218
 
13.8%
3 55632
 
3.1%
2 49603
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1772117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1421664
80.2%
8 245218
 
13.8%
3 55632
 
3.1%
2 49603
 
2.8%

IDADE_CLIENTE
Real number (ℝ)

Distinct99
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.330673
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:44.753125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q131
median39
Q348
95-th percentile63
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.511816
Coefficient of variation (CV)0.31023078
Kurtosis-0.10701299
Mean40.330673
Median Absolute Deviation (MAD)9
Skewness0.59397818
Sum13844915
Variance156.54554
MonotonicityNot monotonic
2023-05-10T15:35:45.639319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 11541
 
3.4%
36 11416
 
3.3%
34 11328
 
3.3%
39 11200
 
3.3%
37 11104
 
3.2%
35 11049
 
3.2%
38 11039
 
3.2%
32 10933
 
3.2%
31 10724
 
3.1%
40 10341
 
3.0%
Other values (89) 232610
67.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
2 4
 
< 0.1%
3 6
< 0.1%
4 3
 
< 0.1%
5 7
< 0.1%
6 6
< 0.1%
7 14
< 0.1%
8 14
< 0.1%
9 7
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
96 1
 
< 0.1%
95 3
 
< 0.1%
94 1
 
< 0.1%
93 3
 
< 0.1%
92 6
< 0.1%
91 13
< 0.1%
90 9
< 0.1%
89 10
< 0.1%

PRODUTO
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
AUTOMOVEIS
245218 
IMOVEIS
48476 
MOTOCICLETAS
42447 
CAMINHÕES
 
7156

Length

Max length12
Median length10
Mean length9.8028238
Min length7

Characters and Unicode

Total characters3365280
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMOTOCICLETAS
2nd rowAUTOMOVEIS
3rd rowCAMINHÕES
4th rowAUTOMOVEIS
5th rowAUTOMOVEIS

Common Values

ValueCountFrequency (%)
AUTOMOVEIS 245218
71.4%
IMOVEIS 48476
 
14.1%
MOTOCICLETAS 42447
 
12.4%
CAMINHÕES 7156
 
2.1%

Length

2023-05-10T15:35:46.426023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T15:35:47.211976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
automoveis 245218
71.4%
imoveis 48476
 
14.1%
motocicletas 42447
 
12.4%
caminhões 7156
 
2.1%

Most occurring characters

ValueCountFrequency (%)
O 623806
18.5%
I 391773
11.6%
M 343297
10.2%
E 343297
10.2%
S 343297
10.2%
T 330112
9.8%
A 294821
8.8%
V 293694
8.7%
U 245218
 
7.3%
C 92050
 
2.7%
Other values (4) 63915
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3365280
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 623806
18.5%
I 391773
11.6%
M 343297
10.2%
E 343297
10.2%
S 343297
10.2%
T 330112
9.8%
A 294821
8.8%
V 293694
8.7%
U 245218
 
7.3%
C 92050
 
2.7%
Other values (4) 63915
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3365280
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 623806
18.5%
I 391773
11.6%
M 343297
10.2%
E 343297
10.2%
S 343297
10.2%
T 330112
9.8%
A 294821
8.8%
V 293694
8.7%
U 245218
 
7.3%
C 92050
 
2.7%
Other values (4) 63915
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3358124
99.8%
None 7156
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 623806
18.6%
I 391773
11.7%
M 343297
10.2%
E 343297
10.2%
S 343297
10.2%
T 330112
9.8%
A 294821
8.8%
V 293694
8.7%
U 245218
 
7.3%
C 92050
 
2.7%
Other values (3) 56759
 
1.7%
None
ValueCountFrequency (%)
Õ 7156
100.0%

QT_PC_PAGAS
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0904319
Minimum0
Maximum100
Zeros2956
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:47.960523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q35
95-th percentile5
Maximum100
Range100
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.409509
Coefficient of variation (CV)0.34458684
Kurtosis137.26844
Mean4.0904319
Median Absolute Deviation (MAD)0
Skewness1.5708844
Sum1404233
Variance1.9867157
MonotonicityNot monotonic
2023-05-10T15:35:48.731249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 172598
50.3%
4 59290
 
17.3%
3 43081
 
12.5%
2 25275
 
7.4%
1 23582
 
6.9%
6 16036
 
4.7%
0 2956
 
0.9%
7 240
 
0.1%
8 90
 
< 0.1%
9 51
 
< 0.1%
Other values (21) 98
 
< 0.1%
ValueCountFrequency (%)
0 2956
 
0.9%
1 23582
 
6.9%
2 25275
 
7.4%
3 43081
 
12.5%
4 59290
 
17.3%
5 172598
50.3%
6 16036
 
4.7%
7 240
 
0.1%
8 90
 
< 0.1%
9 51
 
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
75 1
< 0.1%
50 1
< 0.1%
47 1
< 0.1%
38 1
< 0.1%
34 1
< 0.1%
29 2
< 0.1%
24 1
< 0.1%

QT_PC_VENCIDAS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7926431
Minimum0
Maximum11
Zeros241626
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:49.433386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3372647
Coefficient of variation (CV)1.6870956
Kurtosis0.65382557
Mean0.7926431
Median Absolute Deviation (MAD)0
Skewness1.403196
Sum272112
Variance1.7882769
MonotonicityNot monotonic
2023-05-10T15:35:50.034682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 241626
70.4%
3 38096
 
11.1%
2 30123
 
8.8%
4 18656
 
5.4%
1 12931
 
3.8%
5 1373
 
0.4%
6 317
 
0.1%
7 157
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
0 241626
70.4%
1 12931
 
3.8%
2 30123
 
8.8%
3 38096
 
11.1%
4 18656
 
5.4%
5 1373
 
0.4%
6 317
 
0.1%
7 157
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 6
 
< 0.1%
8 9
 
< 0.1%
7 157
 
< 0.1%
6 317
 
0.1%
5 1373
 
0.4%
4 18656
5.4%
3 38096
11.1%
2 30123
8.8%

QT_PC_PAGA_ATRASO
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5791108
Minimum0
Maximum6
Zeros218566
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:50.666321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91105301
Coefficient of variation (CV)1.5731929
Kurtosis2.3793899
Mean0.5791108
Median Absolute Deviation (MAD)0
Skewness1.6610949
Sum198807
Variance0.83001759
MonotonicityNot monotonic
2023-05-10T15:35:51.216191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 218566
63.7%
1 72422
 
21.1%
2 34986
 
10.2%
3 13198
 
3.8%
4 3833
 
1.1%
5 265
 
0.1%
6 27
 
< 0.1%
ValueCountFrequency (%)
0 218566
63.7%
1 72422
 
21.1%
2 34986
 
10.2%
3 13198
 
3.8%
4 3833
 
1.1%
5 265
 
0.1%
6 27
 
< 0.1%
ValueCountFrequency (%)
6 27
 
< 0.1%
5 265
 
0.1%
4 3833
 
1.1%
3 13198
 
3.8%
2 34986
 
10.2%
1 72422
 
21.1%
0 218566
63.7%

QT_PC_PAGA_EM_DIA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5113211
Minimum0
Maximum100
Zeros3716
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:52.388161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5449813
Coefficient of variation (CV)0.43999999
Kurtosis92.820075
Mean3.5113211
Median Absolute Deviation (MAD)1
Skewness1.4939897
Sum1205426
Variance2.3869671
MonotonicityNot monotonic
2023-05-10T15:35:53.143625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5 107678
31.4%
4 75434
22.0%
3 53447
15.6%
2 46531
13.6%
1 45711
13.3%
6 10340
 
3.0%
0 3716
 
1.1%
7 220
 
0.1%
8 80
 
< 0.1%
9 46
 
< 0.1%
Other values (20) 94
 
< 0.1%
ValueCountFrequency (%)
0 3716
 
1.1%
1 45711
13.3%
2 46531
13.6%
3 53447
15.6%
4 75434
22.0%
5 107678
31.4%
6 10340
 
3.0%
7 220
 
0.1%
8 80
 
< 0.1%
9 46
 
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
79 1
< 0.1%
77 1
< 0.1%
74 1
< 0.1%
48 1
< 0.1%
47 1
< 0.1%
38 1
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
25 1
< 0.1%

VALOR_PARCELAS_ATRASO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean711.30849
Minimum0
Maximum41667
Zeros241626
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-05-10T15:35:54.023856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31250
95-th percentile3750
Maximum41667
Range41667
Interquartile range (IQR)1250

Descriptive statistics

Standard deviation1435.8407
Coefficient of variation (CV)2.0185907
Kurtosis44.290591
Mean711.30849
Median Absolute Deviation (MAD)0
Skewness4.121667
Sum2.4419007 × 108
Variance2061638.6
MonotonicityNot monotonic
2023-05-10T15:35:54.882193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 241626
70.4%
2000 24899
 
7.3%
2667 18436
 
5.4%
1333 16684
 
4.9%
667 8668
 
2.5%
4000 6205
 
1.8%
2500 5561
 
1.6%
3750 3182
 
0.9%
5000 3074
 
0.9%
5333 2400
 
0.7%
Other values (52) 12562
 
3.7%
ValueCountFrequency (%)
0 241626
70.4%
167 396
 
0.1%
333 1878
 
0.5%
500 735
 
0.2%
556 110
 
< 0.1%
667 8668
 
2.5%
833 29
 
< 0.1%
1000 1840
 
0.5%
1111 314
 
0.1%
1167 3
 
< 0.1%
ValueCountFrequency (%)
41667 1
 
< 0.1%
35000 3
 
< 0.1%
33333 24
< 0.1%
30000 2
 
< 0.1%
27500 1
 
< 0.1%
25000 25
< 0.1%
22500 2
 
< 0.1%
20000 31
< 0.1%
17500 18
 
< 0.1%
16667 46
< 0.1%

Interactions

2023-05-10T15:35:20.344871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:16.236123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:24.483747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:32.545290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:40.885554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:48.833603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:56.645122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:04.467430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:12.389355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:21.233730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:17.321553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:25.362505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:33.431195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:41.783489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:49.735321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:57.498905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:05.335593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:13.283655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:22.117169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:18.238703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:26.263996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:34.317851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:42.715624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:50.604175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:58.385835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:06.226751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:14.176946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:23.448040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:19.150945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:27.150964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:35.165966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:43.537933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:51.451677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:59.271242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:07.086368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:15.024862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:24.314462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:20.069551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:28.036086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:36.069098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:44.402195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:52.306344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:00.149173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:07.960910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:15.906017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:25.167239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:20.948967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:28.937986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:36.953302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:45.283809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:53.165163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:01.005624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:08.852613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:16.798020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:26.016080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:21.794770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:29.824671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:37.824485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:46.184374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:54.024770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:01.885285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:09.737092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:17.683313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:26.914725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:22.701851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:30.742167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:38.741423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:47.101682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:54.925574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:02.778476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:10.655110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:18.571020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:27.787488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:23.598174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:31.669111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:39.611380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:47.985007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:34:55.789826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:03.627824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:11.525471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-10T15:35:19.466672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-10T15:35:55.611907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ID_CLIENTEDIAS_ATIVOMESES_ATIVOIDADE_CLIENTEQT_PC_PAGASQT_PC_VENCIDASQT_PC_PAGA_ATRASOQT_PC_PAGA_EM_DIAVALOR_PARCELAS_ATRASOPRAZO_FINANCIAMENTOVALOR_TABELAPRODUTO
ID_CLIENTE1.000-0.301-0.307-0.005-0.0700.1820.207-0.2120.1560.2380.1760.176
DIAS_ATIVO-0.3011.0000.9970.0340.604-0.7510.0790.500-0.7470.1160.0640.064
MESES_ATIVO-0.3070.9971.0000.0340.602-0.7530.0750.500-0.7480.1190.0690.069
IDADE_CLIENTE-0.0050.0340.0341.0000.009-0.021-0.0060.018-0.0150.0440.0740.074
QT_PC_PAGAS-0.0700.6040.6020.0091.000-0.7900.1170.784-0.7760.0050.0030.003
QT_PC_VENCIDAS0.182-0.751-0.753-0.021-0.7901.000-0.099-0.6540.9850.1040.0740.074
QT_PC_PAGA_ATRASO0.2070.0790.075-0.0060.117-0.0991.000-0.460-0.0980.0810.1040.104
QT_PC_PAGA_EM_DIA-0.2120.5000.5000.0180.784-0.654-0.4601.000-0.6370.0060.0010.001
VALOR_PARCELAS_ATRASO0.156-0.747-0.748-0.015-0.7760.985-0.098-0.6371.0000.0690.1180.118
PRAZO_FINANCIAMENTO0.2380.1160.1190.0440.0050.1040.0810.0060.0691.0000.5470.547
VALOR_TABELA0.1760.0640.0690.0740.0030.0740.1040.0010.1180.5471.0001.000
PRODUTO0.1760.0640.0690.0740.0030.0740.1040.0010.1180.5471.0001.000

Missing values

2023-05-10T15:35:29.083736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-10T15:35:31.714703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_CLIENTEDATA_AQUISICAODIAS_ATIVOMESES_ATIVOPRAZO_FINANCIAMENTOVALOR_TABELAIDADE_CLIENTEPRODUTOQT_PC_PAGASQT_PC_VENCIDASQT_PC_PAGA_ATRASOQT_PC_PAGA_EM_DIAVALOR_PARCELAS_ATRASO
06622118/06/20213311202000042.0MOTOCICLETAS1401667
14698210/04/20181198391208000038.0AUTOMOVEIS50140
25788809/10/2020285912023000075.0CAMINHÕES50320
35191525/06/2019757251208000043.0AUTOMOVEIS50140
45272419/09/2019671221208000066.0AUTOMOVEIS50500
54666523/03/20181216401208000041.0AUTOMOVEIS50050
65027504/02/20198982924030000045.0IMOVEIS10010
75835911/11/202025281208000047.0AUTOMOVEIS50230
84812331/07/201810863612023000041.0CAMINHÕES40220
96511403/05/20217921208000042.0AUTOMOVEIS23112000
ID_CLIENTEDATA_AQUISICAODIAS_ATIVOMESES_ATIVOPRAZO_FINANCIAMENTOVALOR_TABELAIDADE_CLIENTEPRODUTOQT_PC_PAGASQT_PC_VENCIDASQT_PC_PAGA_ATRASOQT_PC_PAGA_EM_DIAVALOR_PARCELAS_ATRASO
3432872796168004/04/201617151208000049.0AUTOMOVEIS33032000
3432882957475010/05/201817451208000044.0AUTOMOVEIS33212000
34328957106068103/11/202017051208000056.0AUTOMOVEIS14012667
3432902886052022/05/201716251208000049.0AUTOMOVEIS33032000
3432912717332028/04/201517661208000066.0AUTOMOVEIS33032000
3432922939316026/01/201816661208000040.0AUTOMOVEIS24022667
3432932509306215/04/20161766608000046.0AUTOMOVEIS24115333
3432943110165030/09/20191636608000029.0AUTOMOVEIS23024000
3432952693048016/01/201516551208000062.0AUTOMOVEIS33032000
3432962787616129/04/201615451208000025.0AUTOMOVEIS33122000